Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and sub...Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and subsurface damage depth(SDD)are crucial indicators for evaluating the surface quality of these materials after grinding.Existing prediction models lack general applicability and do not accurately account for the complex material behavior under grinding conditions.This paper introduces novel models for predicting both surface roughness and SDD in hard and brittle semiconductor materials.The surface roughness model uniquely incorporates the material’s elastic recovery properties,revealing the significant impact of these properties on prediction accuracy.The SDD model is distinguished by its analysis of the interactions between abrasive grits and the workpiece,as well as the mechanisms governing stress-induced damage evolution.The surface roughness model and SDD model both establish a stable relationship with the grit depth of cut(GDC).Additionally,we have developed an analytical relationship between the GDC and grinding process parameters.This,in turn,enables the establishment of an analytical framework for predicting surface roughness and SDD based on grinding process parameters,which cannot be achieved by previous models.The models were validated through systematic experiments on three different semiconductor materials,demonstrating excellent agreement with experimental data,with prediction errors of 6.3%for surface roughness and6.9%for SDD.Additionally,this study identifies variations in elastic recovery and material plasticity as critical factors influencing surface roughness and SDD across different materials.These findings significantly advance the accuracy of predictive models and broaden their applicability for grinding hard and brittle semiconductor materials.展开更多
BACKGROUND The trend of risk prediction models for diabetic peripheral neuropathy(DPN)is increasing,but few studies focus on the quality of the model and its practical application.AIM To conduct a comprehensive system...BACKGROUND The trend of risk prediction models for diabetic peripheral neuropathy(DPN)is increasing,but few studies focus on the quality of the model and its practical application.AIM To conduct a comprehensive systematic review and rigorous evaluation of prediction models for DPN.METHODS A meticulous search was conducted in PubMed,EMBASE,Cochrane,CNKI,Wang Fang DATA,and VIP Database to identify studies published until October 2023.The included and excluded criteria were applied by the researchers to screen the literature.Two investigators independently extracted data and assessed the quality using a data extraction form and a bias risk assessment tool.Disagreements were resolved through consultation with a third investigator.Data from the included studies were extracted utilizing the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies.Additionally,the bias risk and applicability of the models were evaluated by the Prediction Model Risk of Bias Assessment Tool.RESULTS The systematic review included 14 studies with a total of 26 models.The area under the receiver operating characteristic curve of the 26 models was 0.629-0.938.All studies had high risks of bias,mainly due to participants,outcomes,and analysis.The most common predictors included glycated hemoglobin,age,duration of diabetes,lipid abnormalities,and fasting blood glucose.CONCLUSION The predictor model presented good differentiation,calibration,but there were significant methodological flaws and high risk of bias.Future studies should focus on improving the study design and study report,updating the model and verifying its adaptability and feasibility in clinical practice.展开更多
Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential S...Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.展开更多
BACKGROUND Patients with cirrhosis with hepatopulmonary syndrome(HPS)have a poorer prognosis.The disease has a subtle onset,symptoms are easily masked,clinical attention is insufficient,and misdiagnosis rates are high...BACKGROUND Patients with cirrhosis with hepatopulmonary syndrome(HPS)have a poorer prognosis.The disease has a subtle onset,symptoms are easily masked,clinical attention is insufficient,and misdiagnosis rates are high.AIM To compare the clinical characteristics of patients with cirrhosis,cirrhosis combined with intrapulmonary vascular dilatation(IPVD),and HPS,and to establish predictive models for IPVD and HPS.METHODS Patients with cirrhosis were prospectively screened at a liver-specialized university teaching hospital.Clinical information and blood samples were collected,and biomarker levels in blood samples were measured.Patients with cirrhosis were divided into three groups:Those with pure cirrhosis,those with combined IPVD,and those with HPS based on contrast-enhanced transthoracic echocardiography results and the pulmonary alveolar-arterial oxygen gradient values.Univariate logistic regression and Least Absolute Shrinkage and Selection Operator(LASSO)regression methods were utilized to identify risk factors for IPVD and HPS,and nomograms were constructed to predict IPVD and HPS.RESULTS A total of 320 patients were analyzed,with 101 diagnosed with IPVD,of whom 54 were diagnosed with HPS.There were statistically significant differences in clinical parameters among these three groups of patients.Among the tested biomarkers,sphingosine 1 phosphate,angiopoietin-2,and platelet-derived growth factor BB were significantly associated with IPVD and HPS in patients with cirrhosis.Following LASSO logistic regression screening,prediction models for IPVD and HPS were established.The area under the receiver operating characteristic curve for IPVD prediction was 0.792(95%confidence interval[CI]:0.737-0.847),and for HPS prediction was 0.891(95%CI:0.848-0.934).CONCLUSION This study systematically compared the clinical characteristics of patients with cirrhosis,IPVD,and HPS,and constructed predictive models for IPVD and HPS based on clinical parameters and laboratory indicators.These models showed good predictive value for IPVD and HPS in patients with cirrhosis.They can assist clinicians in the early prognosis assessment of patients with cirrhosis,ultimately benefiting the patients.展开更多
This article explores the comparison between the probability method and the least squares method in the design of linear predictive models. It points out that these two approaches have distinct theoretical foundations...This article explores the comparison between the probability method and the least squares method in the design of linear predictive models. It points out that these two approaches have distinct theoretical foundations and can lead to varied or similar results in terms of precision and performance under certain assumptions. The article underlines the importance of comparing these two approaches to choose the one best suited to the context, available data and modeling objectives.展开更多
BACKGROUND Study on influencing factors of gastric retention before endoscopic retrograde cholangiopancreatography(ERCP)background:With the wide application of ERCP,the risk of preoperative gastric retention affects t...BACKGROUND Study on influencing factors of gastric retention before endoscopic retrograde cholangiopancreatography(ERCP)background:With the wide application of ERCP,the risk of preoperative gastric retention affects the smooth progress of the operation.The study found that female,biliary and pancreatic malignant tumor,digestive tract obstruction and other factors are closely related to gastric retention,so the establishment of predictive model is very important to reduce the risk of operation.METHODS A retrospective analysis was conducted on 190 patients admitted to our hospital for ERCP preparation between January 2020 and February 2024.Patient baseline clinical data were collected using an electronic medical record system.Patients were randomly matched in a 1:4 ratio with data from 190 patients during the same period to establish a validation group(n=38)and a modeling group(n=152).Patients in the modeling group were divided into the gastric retention group(n=52)and non-gastric retention group(n=100)based on whether gastric retention occurred preoperatively.General data of patients in the validation group and identify factors influencing preoperative gastric retention in ERCP patients.A predictive model for preoperative gastric retention in ERCP patients was constructed,and calibration curves were used for validation.The receiver operating characteristic(ROC)curve was analyzed to evaluate the predictive value of the model.RESULTS We found no statistically significant difference in general data between the validation group and modeling group(P>0.05).The comparison of age,body mass index,hypertension,and diabetes between the two groups showed no statistically significant difference(P>0.05).However,we noted statistically significant differences in gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction between the two groups(P<0.05).Mul-tivariate logistic regression analysis showed that gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction were independent factors influencing preoperative gastric retention in ERCP patients(P<0.05).The results of logistic regression analysis revealed that gender,primary disease,jaundice,opioid use,and gastroin-testinal obstruction were included in the predictive model for preoperative gastric retention in ERCP patients.The calibration curves in the training set and validation set showed a slope close to 1,indicating good consistency between the predicted risk and actual risk.The ROC analysis results showed that the area under the curve(AUC)of the predictive model for preoperative gastric retention in ERCP patients in the training set was 0.901 with a standard error of 0.023(95%CI:0.8264-0.9567),and the optimal cutoff value was 0.71,with a sensitivity of 87.5 and specificity of 84.2.In the validation set,the AUC of the predictive model was 0.842 with a standard error of 0.013(95%CI:0.8061-0.9216),and the optimal cutoff value was 0.56,with a sensitivity of 56.2 and specificity of 100.0.CONCLUSION Gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction are factors influencing preoperative gastric retention in ERCP patients.A predictive model established based on these factors has high predictive value.展开更多
Routine immunization(RI)of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe.Pakistan being a low-and-middle-income-country(LMIC)has one of the ...Routine immunization(RI)of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe.Pakistan being a low-and-middle-income-country(LMIC)has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases(VPDs).For improving RI coverage,a critical need is to establish potential RI defaulters at an early stage,so that appropriate interventions can be targeted towards such populationwho are identified to be at risk of missing on their scheduled vaccine uptakes.In this paper,a machine learning(ML)based predictivemodel has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors.The predictivemodel uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children.The design of predictive model is based on obtaining optimal results across accuracy,specificity,and sensitivity,to ensure model outcomes remain practically relevant to the problem addressed.Further optimization of predictive model is obtained through selection of significant features and removing data bias.Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit.The results showed that the random forest model achieves the optimal accuracy of 81.9%with 83.6%sensitivity and 80.3%specificity.The main determinants of vaccination coverage were found to be vaccine coverage at birth,parental education,and socioeconomic conditions of the defaulting group.This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.展开更多
BACKGROUND Assessment of liver reserve function(LRF)is essential for predicting the prognosis of patients with chronic liver disease(CLD)and determines the extent of liver resection in patients with hepatocellular car...BACKGROUND Assessment of liver reserve function(LRF)is essential for predicting the prognosis of patients with chronic liver disease(CLD)and determines the extent of liver resection in patients with hepatocellular carcinoma.AIM To establish noninvasive models for LRF assessment based on liver stiffness measurement(LSM)and to evaluate their clinical performance.METHODS A total of 360 patients with compensated CLD were retrospectively analyzed as the training cohort.The new predictive models were established through logistic regression analysis and were validated internally in a prospective cohort(132 patients).RESULTS Our study defined indocyanine green retention rate at 15 min(ICGR15)≥10%as mildly impaired LRF and ICGR15≥20%as severely impaired LRF.We constructed predictive models of LRF,named the mLPaM and sLPaM,which involved only LSM,prothrombin time international normalized ratio to albumin ratio(PTAR),age and model for end-stage liver disease(MELD).The area under the curve of the mLPaM model(0.855,0.872,respectively)and sLPaM model(0.869,0.876,respectively)were higher than that of the methods for MELD,albumin bilirubin grade and PTAR in the two cohorts,and their sensitivity and negative predictive value were the highest among these methods in the training cohort.In addition,the new models showed good sensitivity and accuracy for the diagnosis of LRF impairment in the validation cohort.CONCLUSION The new models had a good predictive performance for LRF and could replace the indocyanine green(ICG)clearance test,especially in patients who are unable to undergo ICG testing.展开更多
Pyrolysis of methyl ricinoleate(MR)can produce undecylenic acid methyl ester and heptanal which are important chemicals.Atomization feeding favors the heat exchange in the pyrolysis process and hence increases the pro...Pyrolysis of methyl ricinoleate(MR)can produce undecylenic acid methyl ester and heptanal which are important chemicals.Atomization feeding favors the heat exchange in the pyrolysis process and hence increases the product yield.Herein,predictive models to characterize the atomization process were developed.The effect of spray distance on Sauter mean diameter(SMD)of atomized MR droplets was examined,with the optimal spray distance to be 40-50 mm.Temperature mainly affected the physical properties of feedstock,with smaller droplet size obtained at increasing temperature.In addition,pressure had significant influence on SMD and higher pressure resulted in smaller atomized droplets.Then,a model for SMD prediction,combining temperature,pressure,spray distance,and structural parameters of nozzle,was developed through dimensionless analysis.The results showed that SMD was a power function of Reynolds number(Re),Ohnesorge number(Oh),and the ratio of spray distance to diameter of swirl chamber in the nozzle(H/dsc),with the exponents of-1.6618,-1.3205 and 0.1038,respectively.The experimental measured SMD was in good agreement with the calculated values,with the error within±15%.Moreover,the droplet size distribution was studied by establishing the relationship between the standard deviation of droplet size and SMD.This study could provide reference to the regulation and optimization of the atomization process in MR pyrolysis.展开更多
BACKGROUND Changes in China's fertility policy have led to a significant increase in older pregnant women.At present,there is a lack of analysis of influencing factors and research on predictive models for postpar...BACKGROUND Changes in China's fertility policy have led to a significant increase in older pregnant women.At present,there is a lack of analysis of influencing factors and research on predictive models for postpartum depression(PPD)in older pregnant women.AIM To analysis the influencing factors and the construction of predictive models for PPD in older pregnant women.METHODS By adopting a cross-sectional survey research design,239 older pregnant women(≥35 years old)who underwent obstetric examinations and gave birth at Suzhou Ninth People's Hospital from February 2022 to July 2023 were selected as the research subjects.When postpartum women of advanced maternal age came to the hospital for follow-up 42 d after birth,the Edinburgh PPD Scale(EPDS)was used to assess the presence of PPD symptoms.The women were divided into a PPD group and a no-PPD group.Two sets of data were collected for analysis,and a prediction model was constructed.The performance of the predictive model was evaluated using receiver operating characteristic(ROC)analysis and the Hosmer-Lemeshow goodness-of-fit test.RESULTS On the 42nd day after delivery,51 of 239 older pregnant women were evaluated with the EPDS scale and found to have depressive symptoms.The incidence rate was 21.34%(51/239).There were statistically significant differences between the PPD group and the no-PPD group in terms of education level(P=0.004),family relationships(P=0.001),pregnancy complications(P=0.019),and mother–infant separation after birth(P=0.002).Multivariate logistic regression analysis showed that a high school education and below,poor family relationships,pregnancy complications,and the separation of the mother and baby after birth were influencing factors for PPD in older pregnant women(P<0.05).Based on the influencing factors,the following model equation was developed:Logit(P)=0.729×education level+0.942×family relationship+1.137×pregnancy complications+1.285×separation of the mother and infant after birth-6.671.The area under the ROC curve of this prediction model was 0.873(95%CI:0.821-0.924),the sensitivity was 0.871,and the specificity was 0.815.The deviation between the value predicted by the model and the actual value through the Hosmer-Lemeshow goodness-of-fit test was not statistically significant(χ^(2)=2.749,P=0.638),indicating that the model did not show an overfitting phenomenon.CONCLUSION The risk of PPD among older pregnant women is influenced by educational level,family relationships,pregnancy complications,and the separation of the mother and baby after birth.A prediction model based on these factors can effectively predict the risk of PPD in older pregnant women.展开更多
Breast cancer is the most prevalent female malignant tumor and significantly threatens the health of affected individuals.1 Recent progress in the identification of the molecular subtypes of breast cancer has ensured ...Breast cancer is the most prevalent female malignant tumor and significantly threatens the health of affected individuals.1 Recent progress in the identification of the molecular subtypes of breast cancer has ensured more personalized and precise treatment strategies.2 This has presented new challenges and opportunities in treatment options and disease prognosis.This Special Issue,titled"Recent advances in breast cancer research",highlights the latest advances in clinical,basic,and translational research on breast cancer.It explores tumor resistance mechanisms and microenvironments to enhance our understanding of drug efficacy and safety.展开更多
This article discusses the innovative use of computed tomography radiomics combined with clinical factors to predict treatment response to first-line transarterial chemoembolization in hepatocellular carcinoma.Zhao et...This article discusses the innovative use of computed tomography radiomics combined with clinical factors to predict treatment response to first-line transarterial chemoembolization in hepatocellular carcinoma.Zhao et al developed a robust predictive model demonstrating high accuracy(area under the curve 0.92 in the training cohort)by integrating venous phase radiomic features with alphafetoprotein levels.This noninvasive approach enables early identification of patients unlikely to benefit from transarterial chemoembolization,allowing a timely transition to alternative therapies such as targeted agents or immunotherapy.Such precision strategies may improve clinical outcomes,optimize resource utilization,and increase survival in advanced hepatocellular carcinoma management.Future studies should emphasize external validation and broader clinical adoption.展开更多
With gastric cancer ranking among the most prevalent and deadly malignancies worldwide,early detection and individualized prognosis remain essential for improving patient outcomes.This letter discusses recent advancem...With gastric cancer ranking among the most prevalent and deadly malignancies worldwide,early detection and individualized prognosis remain essential for improving patient outcomes.This letter discusses recent advancements in arti-ficial intelligence(AI)-driven predictive tools for gastric cancer,emphasizing a computed tomography-based radiomic model that achieved a predictive accuracy of area under the curve of 0.893 for treatment response in advanced cases undergoing neoadjuvant immunochemotherapy.AI offers promising avenues for predictive accuracy and personalized treatment planning in gastric oncology.Additionally,this letter highlights the comparison of these AI tools with tra-ditional methodologies,demonstrating their potential to streamline clinical workflows and address existing gaps in risk stratification and early detection.Furthermore,this letter addresses the ethical considerations and the need for robust clinical-AI collaboration to achieve reliable,transparent,and unbiased outcomes.Strengthening cross-disciplinary efforts will be vital for the responsible and effective deployment of AI in this critical area of oncology.展开更多
Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce,ensuring appropriate staffing levels,and providing high-quality care to patients.The intricacy and variety of c...Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce,ensuring appropriate staffing levels,and providing high-quality care to patients.The intricacy and variety of contemporary healthcare systems and a growing patient populace call for advanced forecasting models.Factors like technological advancements,novel treatment protocols,and the increasing prevalence of chronic illnesses have diminished the efficacy of traditional estimation approaches.Novel forecasting methodologies,including time-series analysis,machine learning,and simulation-based techniques,have been developed to tackle these challenges.Time-series analysis recognizes patterns from past data,whereas machine learning uses extensive datasets to uncover concealed trends.Simulation models are employed to assess diverse scenarios,assisting in proactive adjustments to staffing.These techniques offer distinct advantages,such as the identification of seasonal patterns,the management of large datasets,and the ability to test various assumptions.By integrating these sophisticated models into workforce planning,organizations can optimize staffing,reduce financial waste,and elevate the standard of patient care.As the healthcare field progresses,the utilization of these predictive models will be pivotal for fostering adaptable and resilient workforce management.展开更多
Objective:To evaluate the use of infrared thermography technology for objective and quantitative syndrome differentiation and treatment in traditional Chinese medicine(TCM),specifically in patients with Chaihu Guizhi ...Objective:To evaluate the use of infrared thermography technology for objective and quantitative syndrome differentiation and treatment in traditional Chinese medicine(TCM),specifically in patients with Chaihu Guizhi Ganjiang Decoction syndrome.Methods:Data were collected from over 100 patients diagnosed with Chaihu Guizhi Ganjiang Decoction syndrome at Professor Li Leyu’s endocrinology clinic,Zhongshan Hospital of Traditional Chinese Medicine,Guangdong Province,between April 2021 and April 2022.Body surface temperature data were obtained using the MTI-EXPRO-2013-B infrared thermography system.Principal component analysis(PCA)was applied to differentiate temperature distribution characteristics between genders,and a neural network prediction model was constructed for syndrome diagnosis.Results:Infrared thermography effectively captured surface temperature characteristics of patients with Chaihu Guizhi Ganjiang Decoction syndrome.PCA identified one principal component with a variance explanation rate of 73.953%for females and two principal components with a cumulative variance explanation rate of 77.627%for males.The neural network model demonstrated high predictive performance,with an area under the ROC curve of 0.9743 for the training set and 0.9889 for the validation set.Sensitivity was 1,specificity 0.8636,precision 0.8846,accuracy 0.9333,and the F1 score 0.9388.Conclusion:Infrared thermography provides an innovative,objective,and quantitative method for syndrome differentiation and treatment in TCM.It represents a significant advancement in transitioning from traditional empirical approaches to modern,visualized,and precise diagnosis and treatment.This study underscores the potential of integrating advanced technologies in TCM for enhanced clinical application and modernization.展开更多
BACKGROUND Gastric cancer is one of the most common malignant tumors in the digestive system,ranking sixth in incidence and fourth in mortality worldwide.Since 42.5%of metastatic lymph nodes in gastric cancer belong t...BACKGROUND Gastric cancer is one of the most common malignant tumors in the digestive system,ranking sixth in incidence and fourth in mortality worldwide.Since 42.5%of metastatic lymph nodes in gastric cancer belong to nodule type and peripheral type,the application of imaging diagnosis is restricted.AIM To establish models for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning(ML)algorithms and to evaluate their pre-dictive performance in clinical practice.METHODS Data of a total of 369 patients who underwent radical gastrectomy at the Depart-ment of General Surgery of Affiliated Hospital of Xuzhou Medical University(Xuzhou,China)from March 2016 to November 2019 were collected and retro-spectively analyzed as the training group.In addition,data of 123 patients who underwent radical gastrectomy at the Department of General Surgery of Jining First People’s Hospital(Jining,China)were collected and analyzed as the verifi-cation group.Seven ML models,including decision tree,random forest,support vector machine(SVM),gradient boosting machine,naive Bayes,neural network,and logistic regression,were developed to evaluate the occurrence of lymph node metastasis in patients with gastric cancer.The ML models were established fo-llowing ten cross-validation iterations using the training dataset,and subsequently,each model was assessed using the test dataset.The models’performance was evaluated by comparing the area under the receiver operating characteristic curve of each model.RESULTS Among the seven ML models,except for SVM,the other ones exhibited higher accuracy and reliability,and the influences of various risk factors on the models are intuitive.CONCLUSION The ML models developed exhibit strong predictive capabilities for lymph node metastasis in gastric cancer,which can aid in personalized clinical diagnosis and treatment.展开更多
BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To ...BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To develop a risk prediction model for the pathological upgrading of gastric LGIN to aid clinical diagnosis and treatment.METHODS We retrospectively analyzed data from patients newly diagnosed with gastric LGIN who underwent complete endoscopic resection within 6 months at the First Medical Center of Chinese People’s Liberation Army General Hospital between January 2008 and December 2023.A risk prediction model for the pathological progression of gastric LGIN was constructed and evaluated for accuracy and clinical applicability.RESULTS A total of 171 patients were included in this study:93 patients with high-grade intraepithelial neoplasia or early gastric cancer and 78 with LGIN.The logistic stepwise regression model demonstrated a sensitivity and specificity of 0.868 and 0.800,respectively,while the least absolute shrinkage and selection operator(LASSO)regression model showed sensitivity and specificity values of 0.842 and 0.840,respectively.The area under the curve(AUC)for the logistic model was 0.896,slightly lower than the AUC of 0.904 for the LASSO model.Internal validation with 30%of the data yielded AUC scores of 0.908 for the logistic model and 0.905 for the LASSO model.The LASSO model provided greater utility in clinical decision-making.CONCLUSION A risk prediction model for the pathological upgrading of gastric LGIN based on white-light and magnifying endoscopic features can accurately and effectively guide clinical diagnosis and treatment.展开更多
Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the s...Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.展开更多
Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models...Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.展开更多
BACKGROUND An increasing number of studies to date have found preoperative magnetic resonance imaging(MRI)features valuable in predicting the prognosis of rectal cancer(RC).However,research is still lacking on the cor...BACKGROUND An increasing number of studies to date have found preoperative magnetic resonance imaging(MRI)features valuable in predicting the prognosis of rectal cancer(RC).However,research is still lacking on the correlation between preoperative MRI features and the risk of recurrence after radical resection of RC,urgently necessitating further in-depth exploration.AIM To investigate the correlation between preoperative MRI parameters and the risk of recurrence after radical resection of RC to provide an effective tool for predicting postoperative recurrence.METHODS The data of 90 patients who were diagnosed with RC by surgical pathology and underwent radical surgical resection at the Second Affiliated Hospital of Bengbu Medical University between May 2020 and December 2023 were collected through retrospective analysis.General demographic data,MRI data,and tumor markers levels were collected.According to the reviewed data of patients six months after surgery,the clinicians comprehensively assessed the recurrence risk and divided the patients into high recurrence risk(37 cases)and low recurrence risk(53 cases)groups.Independent sample t-test andχ2 test were used to analyze differences between the two groups.A logistic regression model was used to explore the risk factors of the high recurrence risk group,and a clinical prediction model was constructed.The clinical prediction model is presented in the form of a nomogram.The receiver operating characteristic curve,Hosmer-Lemeshow goodness of fit test,calibration curve,and decision curve analysis were used to evaluate the efficacy of the clinical prediction model.RESULTS The detection of positive extramural vascular invasion through preoperative MRI[odds ratio(OR)=4.29,P=0.045],along with elevated carcinoembryonic antigen(OR=1.08,P=0.041),carbohydrate antigen 125(OR=1.19,P=0.034),and carbohydrate antigen 199(OR=1.27,P<0.001)levels,are independent risk factors for increased postoperative recurrence risk in patients with RC.Furthermore,there was a correlation between magnetic resonance based T staging,magnetic resonance based N staging,and circumferential resection margin results determined by MRI and the postoperative recurrence risk.Additionally,when extramural vascular invasion was integrated with tumor markers,the resulting clinical prediction model more effectively identified patients at high risk for postoperative recurrence,thereby providing robust support for clinical decision-making.CONCLUSION The results of this study indicate that preoperative MRI detection is of great importance for predicting the risk of postoperative recurrence in patients with RC.Monitoring these markers helps clinicians identify patients at high risk,allowing for more aggressive treatment and monitoring strategies to improve patient outcomes.展开更多
基金supported by the National Key Research and Development Program of China(2022YFB3605902)the National Natural Science Foundation of China(52375411,52293402)。
文摘Workpiece rotational grinding is widely used in the ultra-precision machining of hard and brittle semiconductor materials,including single-crystal silicon,silicon carbide,and gallium arsenide.Surface roughness and subsurface damage depth(SDD)are crucial indicators for evaluating the surface quality of these materials after grinding.Existing prediction models lack general applicability and do not accurately account for the complex material behavior under grinding conditions.This paper introduces novel models for predicting both surface roughness and SDD in hard and brittle semiconductor materials.The surface roughness model uniquely incorporates the material’s elastic recovery properties,revealing the significant impact of these properties on prediction accuracy.The SDD model is distinguished by its analysis of the interactions between abrasive grits and the workpiece,as well as the mechanisms governing stress-induced damage evolution.The surface roughness model and SDD model both establish a stable relationship with the grit depth of cut(GDC).Additionally,we have developed an analytical relationship between the GDC and grinding process parameters.This,in turn,enables the establishment of an analytical framework for predicting surface roughness and SDD based on grinding process parameters,which cannot be achieved by previous models.The models were validated through systematic experiments on three different semiconductor materials,demonstrating excellent agreement with experimental data,with prediction errors of 6.3%for surface roughness and6.9%for SDD.Additionally,this study identifies variations in elastic recovery and material plasticity as critical factors influencing surface roughness and SDD across different materials.These findings significantly advance the accuracy of predictive models and broaden their applicability for grinding hard and brittle semiconductor materials.
基金Supported by Capital’s Funds for Health Improvement and Research,No.2024-4-4135.
文摘BACKGROUND The trend of risk prediction models for diabetic peripheral neuropathy(DPN)is increasing,but few studies focus on the quality of the model and its practical application.AIM To conduct a comprehensive systematic review and rigorous evaluation of prediction models for DPN.METHODS A meticulous search was conducted in PubMed,EMBASE,Cochrane,CNKI,Wang Fang DATA,and VIP Database to identify studies published until October 2023.The included and excluded criteria were applied by the researchers to screen the literature.Two investigators independently extracted data and assessed the quality using a data extraction form and a bias risk assessment tool.Disagreements were resolved through consultation with a third investigator.Data from the included studies were extracted utilizing the Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies.Additionally,the bias risk and applicability of the models were evaluated by the Prediction Model Risk of Bias Assessment Tool.RESULTS The systematic review included 14 studies with a total of 26 models.The area under the receiver operating characteristic curve of the 26 models was 0.629-0.938.All studies had high risks of bias,mainly due to participants,outcomes,and analysis.The most common predictors included glycated hemoglobin,age,duration of diabetes,lipid abnormalities,and fasting blood glucose.CONCLUSION The predictor model presented good differentiation,calibration,but there were significant methodological flaws and high risk of bias.Future studies should focus on improving the study design and study report,updating the model and verifying its adaptability and feasibility in clinical practice.
文摘Malaria remains a major public health challenge necessitating accurate predictive models to inform effective intervention strategies in Sierra Leone. This study compares the performance of Holt-Winters’ Exponential Smoothing, Harmonic, and Artificial Neural Network (ANN) models using data from January 2018 to December 2023, incorporating both historical case records from Sierra Leone’s Health Management Information System (HMIS) and meteorological variables including humidity, precipitation, and temperature. The ANN model demonstrated superior performance, achieving a Mean Absolute Percentage Error (MAPE) of 4.74% before including climatic variables. This was further reduced to 3.9% with the inclusion of climatic variables, outperforming traditional models like Holt-Winters and Harmonic, which yielded MAPEs of 22.53% and 17.90% respectively. The ANN’s success is attributed to its ability to capture complex, non-linear relationships in the data, particularly when enhanced with relevant climatic variables. Using the optimized ANN model, we forecasted malaria cases for the next 24 months, predicting a steady increase from January 2024 to December 2025, with seasonal peaks. This study underscores the potential of machine learning approaches, particularly ANNs, in epidemiological modelling and highlights the importance of integrating environmental factors into malaria prediction models, recommending the ANN model for informing more targeted and efficient malaria control strategies to improve public health outcomes in Sierra Leone and similar settings.
基金Supported by the National Key Research and Development Program of China,No.2022YFC2305002Beijing Natural Science Foundation,No.7232079+1 种基金Middle-aged and Young Talent Incubation Programs(Clinical Research)of Beijing Youan Hospital,No.BJYAYY-YN2022-12,No.BJYAYY-YN2022-13,and No.BJYAYY-YN2022-01the China Postdoctoral Science Foundation,No.2023M732410 and No.2024T170595.
文摘BACKGROUND Patients with cirrhosis with hepatopulmonary syndrome(HPS)have a poorer prognosis.The disease has a subtle onset,symptoms are easily masked,clinical attention is insufficient,and misdiagnosis rates are high.AIM To compare the clinical characteristics of patients with cirrhosis,cirrhosis combined with intrapulmonary vascular dilatation(IPVD),and HPS,and to establish predictive models for IPVD and HPS.METHODS Patients with cirrhosis were prospectively screened at a liver-specialized university teaching hospital.Clinical information and blood samples were collected,and biomarker levels in blood samples were measured.Patients with cirrhosis were divided into three groups:Those with pure cirrhosis,those with combined IPVD,and those with HPS based on contrast-enhanced transthoracic echocardiography results and the pulmonary alveolar-arterial oxygen gradient values.Univariate logistic regression and Least Absolute Shrinkage and Selection Operator(LASSO)regression methods were utilized to identify risk factors for IPVD and HPS,and nomograms were constructed to predict IPVD and HPS.RESULTS A total of 320 patients were analyzed,with 101 diagnosed with IPVD,of whom 54 were diagnosed with HPS.There were statistically significant differences in clinical parameters among these three groups of patients.Among the tested biomarkers,sphingosine 1 phosphate,angiopoietin-2,and platelet-derived growth factor BB were significantly associated with IPVD and HPS in patients with cirrhosis.Following LASSO logistic regression screening,prediction models for IPVD and HPS were established.The area under the receiver operating characteristic curve for IPVD prediction was 0.792(95%confidence interval[CI]:0.737-0.847),and for HPS prediction was 0.891(95%CI:0.848-0.934).CONCLUSION This study systematically compared the clinical characteristics of patients with cirrhosis,IPVD,and HPS,and constructed predictive models for IPVD and HPS based on clinical parameters and laboratory indicators.These models showed good predictive value for IPVD and HPS in patients with cirrhosis.They can assist clinicians in the early prognosis assessment of patients with cirrhosis,ultimately benefiting the patients.
文摘This article explores the comparison between the probability method and the least squares method in the design of linear predictive models. It points out that these two approaches have distinct theoretical foundations and can lead to varied or similar results in terms of precision and performance under certain assumptions. The article underlines the importance of comparing these two approaches to choose the one best suited to the context, available data and modeling objectives.
文摘BACKGROUND Study on influencing factors of gastric retention before endoscopic retrograde cholangiopancreatography(ERCP)background:With the wide application of ERCP,the risk of preoperative gastric retention affects the smooth progress of the operation.The study found that female,biliary and pancreatic malignant tumor,digestive tract obstruction and other factors are closely related to gastric retention,so the establishment of predictive model is very important to reduce the risk of operation.METHODS A retrospective analysis was conducted on 190 patients admitted to our hospital for ERCP preparation between January 2020 and February 2024.Patient baseline clinical data were collected using an electronic medical record system.Patients were randomly matched in a 1:4 ratio with data from 190 patients during the same period to establish a validation group(n=38)and a modeling group(n=152).Patients in the modeling group were divided into the gastric retention group(n=52)and non-gastric retention group(n=100)based on whether gastric retention occurred preoperatively.General data of patients in the validation group and identify factors influencing preoperative gastric retention in ERCP patients.A predictive model for preoperative gastric retention in ERCP patients was constructed,and calibration curves were used for validation.The receiver operating characteristic(ROC)curve was analyzed to evaluate the predictive value of the model.RESULTS We found no statistically significant difference in general data between the validation group and modeling group(P>0.05).The comparison of age,body mass index,hypertension,and diabetes between the two groups showed no statistically significant difference(P>0.05).However,we noted statistically significant differences in gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction between the two groups(P<0.05).Mul-tivariate logistic regression analysis showed that gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction were independent factors influencing preoperative gastric retention in ERCP patients(P<0.05).The results of logistic regression analysis revealed that gender,primary disease,jaundice,opioid use,and gastroin-testinal obstruction were included in the predictive model for preoperative gastric retention in ERCP patients.The calibration curves in the training set and validation set showed a slope close to 1,indicating good consistency between the predicted risk and actual risk.The ROC analysis results showed that the area under the curve(AUC)of the predictive model for preoperative gastric retention in ERCP patients in the training set was 0.901 with a standard error of 0.023(95%CI:0.8264-0.9567),and the optimal cutoff value was 0.71,with a sensitivity of 87.5 and specificity of 84.2.In the validation set,the AUC of the predictive model was 0.842 with a standard error of 0.013(95%CI:0.8061-0.9216),and the optimal cutoff value was 0.56,with a sensitivity of 56.2 and specificity of 100.0.CONCLUSION Gender,primary disease,jaundice,opioid use,and gastrointestinal obstruction are factors influencing preoperative gastric retention in ERCP patients.A predictive model established based on these factors has high predictive value.
基金This study was funded by GCRF UK and was carried out as part of project CoNTINuE-Capacity building in technology-driven innovation in healthcare.
文摘Routine immunization(RI)of children is the most effective and timely public health intervention for decreasing child mortality rates around the globe.Pakistan being a low-and-middle-income-country(LMIC)has one of the highest child mortality rates in the world occurring mainly due to vaccine-preventable diseases(VPDs).For improving RI coverage,a critical need is to establish potential RI defaulters at an early stage,so that appropriate interventions can be targeted towards such populationwho are identified to be at risk of missing on their scheduled vaccine uptakes.In this paper,a machine learning(ML)based predictivemodel has been proposed to predict defaulting and non-defaulting children on upcoming immunization visits and examine the effect of its underlying contributing factors.The predictivemodel uses data obtained from Paigham-e-Sehat study having immunization records of 3,113 children.The design of predictive model is based on obtaining optimal results across accuracy,specificity,and sensitivity,to ensure model outcomes remain practically relevant to the problem addressed.Further optimization of predictive model is obtained through selection of significant features and removing data bias.Nine machine learning algorithms were applied for prediction of defaulting children for the next immunization visit.The results showed that the random forest model achieves the optimal accuracy of 81.9%with 83.6%sensitivity and 80.3%specificity.The main determinants of vaccination coverage were found to be vaccine coverage at birth,parental education,and socioeconomic conditions of the defaulting group.This information can assist relevant policy makers to take proactive and effective measures for developing evidence based targeted and timely interventions for defaulting children.
基金Startup Fund for Scientific Research of Fujian Medical University,No.2018QH1052Fujian Health Research Talents Training Program,No.2019-1-42.
文摘BACKGROUND Assessment of liver reserve function(LRF)is essential for predicting the prognosis of patients with chronic liver disease(CLD)and determines the extent of liver resection in patients with hepatocellular carcinoma.AIM To establish noninvasive models for LRF assessment based on liver stiffness measurement(LSM)and to evaluate their clinical performance.METHODS A total of 360 patients with compensated CLD were retrospectively analyzed as the training cohort.The new predictive models were established through logistic regression analysis and were validated internally in a prospective cohort(132 patients).RESULTS Our study defined indocyanine green retention rate at 15 min(ICGR15)≥10%as mildly impaired LRF and ICGR15≥20%as severely impaired LRF.We constructed predictive models of LRF,named the mLPaM and sLPaM,which involved only LSM,prothrombin time international normalized ratio to albumin ratio(PTAR),age and model for end-stage liver disease(MELD).The area under the curve of the mLPaM model(0.855,0.872,respectively)and sLPaM model(0.869,0.876,respectively)were higher than that of the methods for MELD,albumin bilirubin grade and PTAR in the two cohorts,and their sensitivity and negative predictive value were the highest among these methods in the training cohort.In addition,the new models showed good sensitivity and accuracy for the diagnosis of LRF impairment in the validation cohort.CONCLUSION The new models had a good predictive performance for LRF and could replace the indocyanine green(ICG)clearance test,especially in patients who are unable to undergo ICG testing.
基金the National Natural Science Foundation of China(grant number 21776261)the Zhejiang Province Public Welfare Technology Application Research Project(grant number 2017C31016)the China Postdoctoral Science Foundation(grant number 2017M612029)。
文摘Pyrolysis of methyl ricinoleate(MR)can produce undecylenic acid methyl ester and heptanal which are important chemicals.Atomization feeding favors the heat exchange in the pyrolysis process and hence increases the product yield.Herein,predictive models to characterize the atomization process were developed.The effect of spray distance on Sauter mean diameter(SMD)of atomized MR droplets was examined,with the optimal spray distance to be 40-50 mm.Temperature mainly affected the physical properties of feedstock,with smaller droplet size obtained at increasing temperature.In addition,pressure had significant influence on SMD and higher pressure resulted in smaller atomized droplets.Then,a model for SMD prediction,combining temperature,pressure,spray distance,and structural parameters of nozzle,was developed through dimensionless analysis.The results showed that SMD was a power function of Reynolds number(Re),Ohnesorge number(Oh),and the ratio of spray distance to diameter of swirl chamber in the nozzle(H/dsc),with the exponents of-1.6618,-1.3205 and 0.1038,respectively.The experimental measured SMD was in good agreement with the calculated values,with the error within±15%.Moreover,the droplet size distribution was studied by establishing the relationship between the standard deviation of droplet size and SMD.This study could provide reference to the regulation and optimization of the atomization process in MR pyrolysis.
基金This study was reviewed and approved by the Ethics Committee of Suzhou Ninth People's Hospital.
文摘BACKGROUND Changes in China's fertility policy have led to a significant increase in older pregnant women.At present,there is a lack of analysis of influencing factors and research on predictive models for postpartum depression(PPD)in older pregnant women.AIM To analysis the influencing factors and the construction of predictive models for PPD in older pregnant women.METHODS By adopting a cross-sectional survey research design,239 older pregnant women(≥35 years old)who underwent obstetric examinations and gave birth at Suzhou Ninth People's Hospital from February 2022 to July 2023 were selected as the research subjects.When postpartum women of advanced maternal age came to the hospital for follow-up 42 d after birth,the Edinburgh PPD Scale(EPDS)was used to assess the presence of PPD symptoms.The women were divided into a PPD group and a no-PPD group.Two sets of data were collected for analysis,and a prediction model was constructed.The performance of the predictive model was evaluated using receiver operating characteristic(ROC)analysis and the Hosmer-Lemeshow goodness-of-fit test.RESULTS On the 42nd day after delivery,51 of 239 older pregnant women were evaluated with the EPDS scale and found to have depressive symptoms.The incidence rate was 21.34%(51/239).There were statistically significant differences between the PPD group and the no-PPD group in terms of education level(P=0.004),family relationships(P=0.001),pregnancy complications(P=0.019),and mother–infant separation after birth(P=0.002).Multivariate logistic regression analysis showed that a high school education and below,poor family relationships,pregnancy complications,and the separation of the mother and baby after birth were influencing factors for PPD in older pregnant women(P<0.05).Based on the influencing factors,the following model equation was developed:Logit(P)=0.729×education level+0.942×family relationship+1.137×pregnancy complications+1.285×separation of the mother and infant after birth-6.671.The area under the ROC curve of this prediction model was 0.873(95%CI:0.821-0.924),the sensitivity was 0.871,and the specificity was 0.815.The deviation between the value predicted by the model and the actual value through the Hosmer-Lemeshow goodness-of-fit test was not statistically significant(χ^(2)=2.749,P=0.638),indicating that the model did not show an overfitting phenomenon.CONCLUSION The risk of PPD among older pregnant women is influenced by educational level,family relationships,pregnancy complications,and the separation of the mother and baby after birth.A prediction model based on these factors can effectively predict the risk of PPD in older pregnant women.
文摘Breast cancer is the most prevalent female malignant tumor and significantly threatens the health of affected individuals.1 Recent progress in the identification of the molecular subtypes of breast cancer has ensured more personalized and precise treatment strategies.2 This has presented new challenges and opportunities in treatment options and disease prognosis.This Special Issue,titled"Recent advances in breast cancer research",highlights the latest advances in clinical,basic,and translational research on breast cancer.It explores tumor resistance mechanisms and microenvironments to enhance our understanding of drug efficacy and safety.
文摘This article discusses the innovative use of computed tomography radiomics combined with clinical factors to predict treatment response to first-line transarterial chemoembolization in hepatocellular carcinoma.Zhao et al developed a robust predictive model demonstrating high accuracy(area under the curve 0.92 in the training cohort)by integrating venous phase radiomic features with alphafetoprotein levels.This noninvasive approach enables early identification of patients unlikely to benefit from transarterial chemoembolization,allowing a timely transition to alternative therapies such as targeted agents or immunotherapy.Such precision strategies may improve clinical outcomes,optimize resource utilization,and increase survival in advanced hepatocellular carcinoma management.Future studies should emphasize external validation and broader clinical adoption.
文摘With gastric cancer ranking among the most prevalent and deadly malignancies worldwide,early detection and individualized prognosis remain essential for improving patient outcomes.This letter discusses recent advancements in arti-ficial intelligence(AI)-driven predictive tools for gastric cancer,emphasizing a computed tomography-based radiomic model that achieved a predictive accuracy of area under the curve of 0.893 for treatment response in advanced cases undergoing neoadjuvant immunochemotherapy.AI offers promising avenues for predictive accuracy and personalized treatment planning in gastric oncology.Additionally,this letter highlights the comparison of these AI tools with tra-ditional methodologies,demonstrating their potential to streamline clinical workflows and address existing gaps in risk stratification and early detection.Furthermore,this letter addresses the ethical considerations and the need for robust clinical-AI collaboration to achieve reliable,transparent,and unbiased outcomes.Strengthening cross-disciplinary efforts will be vital for the responsible and effective deployment of AI in this critical area of oncology.
文摘Accurate prediction of nurse demand plays a crucial role in efficiently planning the healthcare workforce,ensuring appropriate staffing levels,and providing high-quality care to patients.The intricacy and variety of contemporary healthcare systems and a growing patient populace call for advanced forecasting models.Factors like technological advancements,novel treatment protocols,and the increasing prevalence of chronic illnesses have diminished the efficacy of traditional estimation approaches.Novel forecasting methodologies,including time-series analysis,machine learning,and simulation-based techniques,have been developed to tackle these challenges.Time-series analysis recognizes patterns from past data,whereas machine learning uses extensive datasets to uncover concealed trends.Simulation models are employed to assess diverse scenarios,assisting in proactive adjustments to staffing.These techniques offer distinct advantages,such as the identification of seasonal patterns,the management of large datasets,and the ability to test various assumptions.By integrating these sophisticated models into workforce planning,organizations can optimize staffing,reduce financial waste,and elevate the standard of patient care.As the healthcare field progresses,the utilization of these predictive models will be pivotal for fostering adaptable and resilient workforce management.
基金Zhongshan Science and Technology Bureau Project“The Application of Infrared Thermography in the Syndrome Differentiation of Chaihu Guizhi Ganjiang Decoction”(Project No.2021B1066)Zhongshan Science and Technology Bureau Project“Exploring the Diagnostic Approach of the TCM Syndrome Type‘Chaihu Guizhi Ganjiang Decoction’Based on Infrared Thermal Imaging Systems and Digital Modeling Methods of Ancient and Modern Literature”(Project No.2022B1131)。
文摘Objective:To evaluate the use of infrared thermography technology for objective and quantitative syndrome differentiation and treatment in traditional Chinese medicine(TCM),specifically in patients with Chaihu Guizhi Ganjiang Decoction syndrome.Methods:Data were collected from over 100 patients diagnosed with Chaihu Guizhi Ganjiang Decoction syndrome at Professor Li Leyu’s endocrinology clinic,Zhongshan Hospital of Traditional Chinese Medicine,Guangdong Province,between April 2021 and April 2022.Body surface temperature data were obtained using the MTI-EXPRO-2013-B infrared thermography system.Principal component analysis(PCA)was applied to differentiate temperature distribution characteristics between genders,and a neural network prediction model was constructed for syndrome diagnosis.Results:Infrared thermography effectively captured surface temperature characteristics of patients with Chaihu Guizhi Ganjiang Decoction syndrome.PCA identified one principal component with a variance explanation rate of 73.953%for females and two principal components with a cumulative variance explanation rate of 77.627%for males.The neural network model demonstrated high predictive performance,with an area under the ROC curve of 0.9743 for the training set and 0.9889 for the validation set.Sensitivity was 1,specificity 0.8636,precision 0.8846,accuracy 0.9333,and the F1 score 0.9388.Conclusion:Infrared thermography provides an innovative,objective,and quantitative method for syndrome differentiation and treatment in TCM.It represents a significant advancement in transitioning from traditional empirical approaches to modern,visualized,and precise diagnosis and treatment.This study underscores the potential of integrating advanced technologies in TCM for enhanced clinical application and modernization.
文摘BACKGROUND Gastric cancer is one of the most common malignant tumors in the digestive system,ranking sixth in incidence and fourth in mortality worldwide.Since 42.5%of metastatic lymph nodes in gastric cancer belong to nodule type and peripheral type,the application of imaging diagnosis is restricted.AIM To establish models for predicting the risk of lymph node metastasis in gastric cancer patients using machine learning(ML)algorithms and to evaluate their pre-dictive performance in clinical practice.METHODS Data of a total of 369 patients who underwent radical gastrectomy at the Depart-ment of General Surgery of Affiliated Hospital of Xuzhou Medical University(Xuzhou,China)from March 2016 to November 2019 were collected and retro-spectively analyzed as the training group.In addition,data of 123 patients who underwent radical gastrectomy at the Department of General Surgery of Jining First People’s Hospital(Jining,China)were collected and analyzed as the verifi-cation group.Seven ML models,including decision tree,random forest,support vector machine(SVM),gradient boosting machine,naive Bayes,neural network,and logistic regression,were developed to evaluate the occurrence of lymph node metastasis in patients with gastric cancer.The ML models were established fo-llowing ten cross-validation iterations using the training dataset,and subsequently,each model was assessed using the test dataset.The models’performance was evaluated by comparing the area under the receiver operating characteristic curve of each model.RESULTS Among the seven ML models,except for SVM,the other ones exhibited higher accuracy and reliability,and the influences of various risk factors on the models are intuitive.CONCLUSION The ML models developed exhibit strong predictive capabilities for lymph node metastasis in gastric cancer,which can aid in personalized clinical diagnosis and treatment.
基金Supported by the National Key Research and Development Program of China,No.2022YFC2503600。
文摘BACKGROUND The discrepancy between endoscopic biopsy pathology and the overall pathology of gastric low-grade intraepithelial neoplasia(LGIN)presents challenges in developing diagnostic and treatment protocols.AIM To develop a risk prediction model for the pathological upgrading of gastric LGIN to aid clinical diagnosis and treatment.METHODS We retrospectively analyzed data from patients newly diagnosed with gastric LGIN who underwent complete endoscopic resection within 6 months at the First Medical Center of Chinese People’s Liberation Army General Hospital between January 2008 and December 2023.A risk prediction model for the pathological progression of gastric LGIN was constructed and evaluated for accuracy and clinical applicability.RESULTS A total of 171 patients were included in this study:93 patients with high-grade intraepithelial neoplasia or early gastric cancer and 78 with LGIN.The logistic stepwise regression model demonstrated a sensitivity and specificity of 0.868 and 0.800,respectively,while the least absolute shrinkage and selection operator(LASSO)regression model showed sensitivity and specificity values of 0.842 and 0.840,respectively.The area under the curve(AUC)for the logistic model was 0.896,slightly lower than the AUC of 0.904 for the LASSO model.Internal validation with 30%of the data yielded AUC scores of 0.908 for the logistic model and 0.905 for the LASSO model.The LASSO model provided greater utility in clinical decision-making.CONCLUSION A risk prediction model for the pathological upgrading of gastric LGIN based on white-light and magnifying endoscopic features can accurately and effectively guide clinical diagnosis and treatment.
基金supported in part by the National Natural Science Foundation of China under Grant 52077002。
文摘Model predictive control(MPC)has been deemed as an attractive control method in motor drives by virtue of its simple structure,convenient multi-objective optimization,and satisfactory dynamic performance.However,the strong reliance on mathematical models seriously restrains its practical application.Therefore,improving the robustness of MPC has attained significant attentions in the last two decades,followed by which,model-free predictive control(MFPC)comes into existence.This article aims to reveal the current state of MFPC strategies for motor drives and give the categorization from the perspective of implementation.Based on this review,the principles of the reported MFPC strategies are introduced in detail,as well as the challenges encountered in technology realization.In addition,some of typical and important concepts are experimentally validated via case studies to evaluate the performance and highlight their features.Finally,the future trends of MFPC are discussed based on the current state and reported developments.
文摘Traffic forecasting with high precision aids Intelligent Transport Systems(ITS)in formulating and optimizing traffic management strategies.The algorithms used for tuning the hyperparameters of the deep learning models often have accurate results at the expense of high computational complexity.To address this problem,this paper uses the Tree-structured Parzen Estimator(TPE)to tune the hyperparameters of the Long Short-term Memory(LSTM)deep learning framework.The Tree-structured Parzen Estimator(TPE)uses a probabilistic approach with an adaptive searching mechanism by classifying the objective function values into good and bad samples.This ensures fast convergence in tuning the hyperparameter values in the deep learning model for performing prediction while still maintaining a certain degree of accuracy.It also overcomes the problem of converging to local optima and avoids timeconsuming random search and,therefore,avoids high computational complexity in prediction accuracy.The proposed scheme first performs data smoothing and normalization on the input data,which is then fed to the input of the TPE for tuning the hyperparameters.The traffic data is then input to the LSTM model with tuned parameters to perform the traffic prediction.The three optimizers:Adaptive Moment Estimation(Adam),Root Mean Square Propagation(RMSProp),and Stochastic Gradient Descend with Momentum(SGDM)are also evaluated for accuracy prediction and the best optimizer is then chosen for final traffic prediction in TPE-LSTM model.Simulation results verify the effectiveness of the proposed model in terms of accuracy of prediction over the benchmark schemes.
文摘BACKGROUND An increasing number of studies to date have found preoperative magnetic resonance imaging(MRI)features valuable in predicting the prognosis of rectal cancer(RC).However,research is still lacking on the correlation between preoperative MRI features and the risk of recurrence after radical resection of RC,urgently necessitating further in-depth exploration.AIM To investigate the correlation between preoperative MRI parameters and the risk of recurrence after radical resection of RC to provide an effective tool for predicting postoperative recurrence.METHODS The data of 90 patients who were diagnosed with RC by surgical pathology and underwent radical surgical resection at the Second Affiliated Hospital of Bengbu Medical University between May 2020 and December 2023 were collected through retrospective analysis.General demographic data,MRI data,and tumor markers levels were collected.According to the reviewed data of patients six months after surgery,the clinicians comprehensively assessed the recurrence risk and divided the patients into high recurrence risk(37 cases)and low recurrence risk(53 cases)groups.Independent sample t-test andχ2 test were used to analyze differences between the two groups.A logistic regression model was used to explore the risk factors of the high recurrence risk group,and a clinical prediction model was constructed.The clinical prediction model is presented in the form of a nomogram.The receiver operating characteristic curve,Hosmer-Lemeshow goodness of fit test,calibration curve,and decision curve analysis were used to evaluate the efficacy of the clinical prediction model.RESULTS The detection of positive extramural vascular invasion through preoperative MRI[odds ratio(OR)=4.29,P=0.045],along with elevated carcinoembryonic antigen(OR=1.08,P=0.041),carbohydrate antigen 125(OR=1.19,P=0.034),and carbohydrate antigen 199(OR=1.27,P<0.001)levels,are independent risk factors for increased postoperative recurrence risk in patients with RC.Furthermore,there was a correlation between magnetic resonance based T staging,magnetic resonance based N staging,and circumferential resection margin results determined by MRI and the postoperative recurrence risk.Additionally,when extramural vascular invasion was integrated with tumor markers,the resulting clinical prediction model more effectively identified patients at high risk for postoperative recurrence,thereby providing robust support for clinical decision-making.CONCLUSION The results of this study indicate that preoperative MRI detection is of great importance for predicting the risk of postoperative recurrence in patients with RC.Monitoring these markers helps clinicians identify patients at high risk,allowing for more aggressive treatment and monitoring strategies to improve patient outcomes.
